Altered spontaneous activity in antisocial personality disorder revealed by regional homogeneity

Different whole-brain functional connectivity correlates of reactive-proactive aggression and callous-unemotional traits in children and adolescents with disruptive behaviors

BACKGROUND

Disruptive behavior in children and adolescents can manifest as reactive aggression and proactive aggression and is modulated by callous-unemotional traits and other comorbidities. Neural correlates of these aggression dimensions or subtypes and comorbid symptoms remain largely unknown. This multi-center study investigated the relationship between resting state functional connectivity (rsFC) and aggression subtypes considering comorbidities.

METHODS

The large sample of children and adolescents aged 8-18 years (n = 207; mean age = 13.30±2.60 years, 150 males) included 118 cases with disruptive behavior (80 with Oppositional Defiant Disorder and/or Conduct Disorder) and 89 controls. Attention-deficit/hyperactivity disorder (ADHD) and anxiety symptom scores were analyzed as covariates when assessing group differences and dimensional aggression effects on hypothesis-free global and local voxel-to-voxel whole-brain rsFC based on functional magnetic resonance imaging at 3 Tesla.

RESULTS

Compared to controls, the cases demonstrated altered rsFC in frontal areas, when anxiety but not ADHD symptoms were controlled for. For cases, reactive and proactive aggression scores were related to global and local rsFC in the central gyrus and precuneus, regions linked to aggression-related impairments. Callous-unemotional trait severity was correlated with ICC in the inferior and middle temporal regions implicated in empathy, emotion, and reward processing. Most observed aggression subtype-specific patterns could only be identified when ADHD and anxiety were controlled for.

CONCLUSIONS

This study clarifies that hypothesis-free brain connectivity measures can disentangle distinct though overlapping dimensions of aggression in youths. Moreover, our results highlight the importance of considering comorbid symptoms to detect aggression-related rsFC alterations in youths.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

BACKGROUND

The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.

METHODS

Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses.

RESULTS

A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β =  - 2.75, p < .001, R²_adj = 0.40; r =  - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF₁₀ > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance.

CONCLUSIONS

Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

OBJECTIVE

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

EVIDENCE REVIEW

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

FINDINGS

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

CONCLUSIONS AND RELEVANCE

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

Functional MRI findings in personality disorders: A review

Personality disorders (PDs) have a prevalence of approximately 10% in the United States, translating to over 30 million people affected in just one country. The true prevalence of these disorders may be even higher, as the paucity of objective diagnostic criteria could be leading to underdiagnosis. Because little is known about the underlying neuropathologies of these disorders, patients are diagnosed using subjective criteria and treated nonspecifically. To better understand the neural aberrancies responsible for these patients' symptoms, a review of functional MRI literature was performed. The findings reveal that each PD is characterized by a unique set of activation changes corresponding to individual structures or specific neural networks. While unique patterns of neural activity are distinguishable within each PD, aberrations of the limbic/paralimbic structures and default mode network are noted across several of them. In addition to identifying valuable activation patterns, this review reveals a void in research pertaining to paranoid, schizoid, histrionic, narcissistic, and dependent PDs. By delineating patterns in PD neuropathology, we can more effectively direct future research efforts toward enhancing objective diagnostic techniques and developing targeted treatment modalities. Furthermore, understanding why patients are manifesting certain symptoms can advance clinical awareness and improve patient outcomes.

Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals

Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequences mentally and physically. This paper is aimed at studying whether the abnormalities of regional homogeneity (ReHo) could be effective features to distinguish individuals with methamphetamine dependence (MAD) from control subjects using machine-learning methods. We made use of resting-state fMRI to measure the regional homogeneity of 41 individuals with MAD and 42 age- and sex-matched control subjects and found that compared with control subjects, individuals with MAD have lower ReHo values in the right medial superior frontal gyrus but higher ReHo values in the right temporal inferior fusiform. In addition, AdaBoost classifier, a pretty effective ensemble learning of machine learning, was employed to classify individuals with MAD from control subjects with abnormal ReHo values. By utilizing the leave-one-out cross-validation method, we got the accuracy more than 84.3%, which means we can almost distinguish individuals with MAD from the control subjects in ReHo values via machine-learning approaches. In a word, our research results suggested that the AdaBoost classifier-neuroimaging approach may be a promising way to find whether a person has been addicted to methamphetamine, and also, this paper shows that resting-state fMRI should be considered as a biomarker, a noninvasive and effective assistant tool for evaluating MAD.

Is there an "antisocial" cerebellum? Evidence from disorders other than autism characterized by abnormal social behaviours

The cerebellum is a hindbrain structure which involvement in functions not related to motor control and planning is being increasingly recognized in the last decades. Studies on Autism Spectrum Disorders (ASD) have reported cerebellar involvement on these conditions characterized by social deficits and repetitive motor behavior patterns. Although such an involvement hints at a possible cerebellar participation in the social domain, the fact that ASD patients present both social and motor deficits impedes drawing any firm conclusion regarding cerebellar involvement in pathological social behaviours, probably influenced by the classical view of the cerebellum as a purely "motor" brain structure. Here, we suggest the cerebellum can be a key node for the production and control of normal and particularly aberrant social behaviours, as indicated by its involvement in other neuropsychiatric disorders which main symptom is deregulated social behaviour. Therefore, in this work, we briefly review cerebellar involvement in social behavior in rodent models, followed by discussing the findings linking the cerebellum to those other psychiatric conditions characterized by defective social behaviours. Finally, possible commonalities between the studies and putative underlying impaired functions will be discussed and experimental approaches both in patients and experimental animals will also be proposed, aimed at stimulating research on the role of the cerebellum in social behaviours and disorders characterized by social impairments, which, if successful, will definitely help reinforcing the proposed cerebellar involvement in the social domain.

A Systematic Literature Review of Neuroimaging of Psychopathic Traits

INTRODUCTION

Core psychopathy is characterized by grandiosity, callousness, manipulativeness, and lack of remorse, empathy, and guilt. It is often comorbid with conduct disorder and antisocial personality disorder (ASPD). Psychopathy is present in forensic as well as prison and general populations. In recent years, an increasing amount of neuroimaging studies has been conducted in order to elucidate the obscure neurobiological etiology of psychopathy. The studies have yielded heterogenous results, and no consensus has been reached.

AIMS

This study systematically reviewed and qualitatively summarized functional and structural neuroimaging studies conducted on individuals with psychopathic traits. Furthermore, this study aimed to evaluate whether the findings from different MRI modalities could be reconciled from a neuroanatomical perspective.

MATERIALS AND METHODS

After the search and auditing processes, 118 neuroimaging studies were included in this systematic literature review. The studies consisted of structural, functional, and diffusion tensor MRI studies.

RESULTS

Psychopathy was associated with numerous neuroanatomical abnormalities. Structurally, gray matter anomalies were seen in frontotemporal, cerebellar, limbic, and paralimbic regions. Associated gray matter volume (GMV) reductions were most pronounced particularly in most of the prefrontal cortex, and temporal gyri including the fusiform gyrus. Also decreased GMV of the amygdalae and hippocampi as well the cingulate and insular cortices were associated with psychopathy, as well as abnormal morphology of the hippocampi, amygdala, and nucleus accumbens. Functionally, psychopathy was associated with dysfunction of the default mode network, which was also linked to poor moral judgment as well as deficient metacognitive and introspective abilities. Second, reduced white matter integrity in the uncinate fasciculus and dorsal cingulum were associated with core psychopathy. Third, emotional detachment was associated with dysfunction of the posterior cerebellum, the human mirror neuron system and the Theory of Mind denoting lack of empathy and persistent failure in integrating affective information into cognition.

CONCLUSIONS

Structural and functional aberrancies involving the limbic and paralimbic systems including reduced integrity of the uncinate fasciculus appear to be associated with core psychopathic features. Furthermore, this review points towards the idea that ASPD and psychopathy might stem from divergent biological processes.

Aberrant functional brain connectome in people with antisocial personality disorder

Antisocial personality disorder (ASPD) is characterised by a disregard for social obligations and callous unconcern for the feelings of others. Studies have demonstrated that ASPD is associated with abnormalities in brain regions and aberrant functional connectivity. In this paper, topological organisation was examined in resting-state fMRI data obtained from 32 ASPD patients and 32 non-ASPD controls. The frequency-dependent functional networks were constructed using wavelet-based correlations over 90 brain regions. The topology of the functional networks of ASPD subjects was analysed via graph theoretical analysis. Furthermore, the abnormal functional connectivity was determined with a network-based statistic (NBS) approach. Our results revealed that, compared with the controls, the ASPD patients exhibited altered topological configuration of the functional connectome in the frequency interval of 0.016–0.031 Hz, as indicated by the increased clustering coefficient and decreased betweenness centrality in the medial superior frontal gyrus, precentral gyrus, Rolandic operculum, superior parietal gyrus, angular gyrus, and middle temporal pole. In addition, the ASPD patients showed increased functional connectivity mainly located in the default-mode network. The present study reveals an aberrant topological organisation of the functional brain network in individuals with ASPD. Our findings provide novel insight into the neuropathological mechanisms of ASPD.

The neural correlates of tic inhibition in Gilles de la Tourette syndrome

Tics in Gilles de la Tourette syndrome (GTS) resemble fragments of normal motor behaviour but appear in an intrusive, repetitive and context-inappropriate manner. Although tics can be voluntarily inhibited on demand, the neural correlates of this process remain unclear. 14 GTS adults without relevant comorbidities participated in this study. First, tic severity and voluntary tic inhibitory capacity were evaluated outside the scanner. Second, patients were examined with resting state functional magnetic resonance imaging (RS-fMRI) in two states, free ticcing and voluntary tic inhibition. Local synchronization of spontaneous fMRI-signal was analysed with regional homogeneity (ReHo) and differences between both states (free ticcing<tic inhibition) were contrasted. Clinical correlations of the resulting differential ReHo parameters between both states and clinical measures of tic frequency, voluntary tic inhibition and premonitory urges were also performed. ReHo of the left inferior frontal gyrus (IFG) was increased during voluntary tic inhibition compared to free ticcing. ReHo increases were positively correlated with participants׳ ability to inhibit their tics during scanning sessions but also outside the scanner. There was no correlation with ratings of premonitory urges. Voluntary tic inhibition is associated with increased ReHo of the left IFG. Premonitory urges are unrelated to this process.